
# importing libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler

# read the file from anaconda cloud, just first 10 rows to see if it works
file_name = 'all_ticks_wide.csv.gz'
sample_df = pd.read_csv(file_name, compression='gzip', nrows = 10)
display(sample_df)


# read full data now
df = pd.read_csv(file_name, compression='gzip')

# Create a parsed version to get rid of the timestamps to prevent not displaying all column issues in anaconda cloud
parsed_timestamp_df = df.drop(df.columns[0], axis = 1)

# Start understanding structure 1 - head and tail
display(df.head())
display(df.tail())


# Sart understanding structure 2 - data types
print(df.dtypes)

timestamp     object
AEFES        float64
AKBNK        float64
AKSA         float64
AKSEN        float64
              ...   
VESTL        float64
YATAS        float64
YKBNK        float64
YUNSA        float64
ZOREN        float64
Length: 61, dtype: object


# Start understanding structure 2 - data types - need to convert timestamp to a date_time format and see if it worked
df['timestamp'] = pd.to_datetime(df['timestamp'])
print(df.dtypes)

timestamp    datetime64[ns, UTC]
AEFES                    float64
AKBNK                    float64
AKSA                     float64
AKSEN                    float64
                    ...         
VESTL                    float64
YATAS                    float64
YKBNK                    float64
YUNSA                    float64
ZOREN                    float64
Length: 61, dtype: object


# Start understanding structure 2 - data types - lets see the full list of stocks
print(parsed_timestamp_df.dtypes)

AEFES    float64
AKBNK    float64
AKSA     float64
AKSEN    float64
ALARK    float64
ALBRK    float64
ANACM    float64
ARCLK    float64
ASELS    float64
ASUZU    float64
AYGAZ    float64
BAGFS    float64
BANVT    float64
BRISA    float64
CCOLA    float64
CEMAS    float64
ECILC    float64
EREGL    float64
FROTO    float64
GARAN    float64
GOODY    float64
GUBRF    float64
HALKB    float64
ICBCT    float64
ISCTR    float64
ISDMR    float64
ISFIN    float64
ISYAT    float64
KAREL    float64
KARSN    float64
KCHOL    float64
KRDMB    float64
KRDMD    float64
MGROS    float64
OTKAR    float64
PARSN    float64
PETKM    float64
PGSUS    float64
PRKME    float64
SAHOL    float64
SASA     float64
SISE     float64
SKBNK    float64
SODA     float64
TCELL    float64
THYAO    float64
TKFEN    float64
TOASO    float64
TRKCM    float64
TSKB     float64
TTKOM    float64
TUKAS    float64
TUPRS    float64
USAK     float64
VAKBN    float64
VESTL    float64
YATAS    float64
YKBNK    float64
YUNSA    float64
ZOREN    float64
dtype: object


# Start understanding structure 3 - lets look at shape
print(df.shape)

(50012, 61)


# Rows and columns consistent with what we see in head and tail
# Start understanding structure 3 - lets look at missing values, first for timestamp then for all values
print(df.isnull().sum())
print(parsed_timestamp_df.isnull().sum())

timestamp       0
AEFES        1881
AKBNK         803
AKSA         1418
AKSEN        1841
             ... 
VESTL        1231
YATAS        3957
YKBNK         787
YUNSA        4484
ZOREN        1205
Length: 61, dtype: int64
AEFES     1881
AKBNK      803
AKSA      1418
AKSEN     1841
ALARK     1677
ALBRK     3150
ANACM     1847
ARCLK      967
ASELS     1209
ASUZU     1579
AYGAZ     1893
BAGFS     1362
BANVT     2061
BRISA     1075
CCOLA     1263
CEMAS     3618
ECILC     1520
EREGL      839
FROTO     1017
GARAN      704
GOODY     1051
GUBRF      955
HALKB      941
ICBCT     5676
ISCTR      791
ISDMR    37785
ISFIN     7135
ISYAT     6828
KAREL     3980
KARSN     1485
KCHOL      919
KRDMB     2480
KRDMD      851
MGROS     1109
OTKAR     1227
PARSN     4687
PETKM      828
PGSUS     4791
PRKME     1546
SAHOL      917
SASA      2379
SISE       922
SKBNK     2742
SODA      1736
TCELL      869
THYAO      730
TKFEN     1082
TOASO     1066
TRKCM     1126
TSKB      1628
TTKOM      935
TUKAS     4083
TUPRS      869
USAK      2353
VAKBN      800
VESTL     1231
YATAS     3957
YKBNK      787
YUNSA     4484
ZOREN     1205
dtype: int64


# Start understanding structure 3 - missing values - we have missing values ranging around 
# 2% to 5% per column yet there are exceptions like ISDMR. lets see their position
nan_matrix = df.isnull()
plt.figure(figsize=(15,10))
sns.heatmap(nan_matrix, cbar=False, cmap='viridis')
plt.show()


# Start understanding structure 3 - missing values - lets fill the values by interpolating and update parsed version as well
df = df.interpolate(method='linear', axis=0)
parsed_timestamp_df = df.drop(df.columns[0], axis = 1)
nan_matrix = df.isnull()
plt.figure(figsize=(15,10))
sns.heatmap(nan_matrix, cbar=False, cmap='viridis')
plt.show()


# Start understanding distribution - summary statistics and histograms
display(df.describe())
df.hist(figsize=(20,15))
plt.tight_layout()
plt.show()


# Start understanding distribution - boxplots
plt.figure(figsize=(20,10))
sns.boxplot(data=df.iloc[:, 1:]) # if timestamp is the first column, exclude it
plt.xticks(rotation=90)
plt.show()


# We have some minimum values that are equal to zero. lets keep that in mind when we select stocks
# Start understanding patterns - time series
plt.figure(figsize=(20,10))
for column in df.columns[1:]:
    plt.plot(df['timestamp'], df[column], label=column)
plt.legend()
plt.show()

/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Creating legend with loc="best" can be slow with large amounts of data.
  fig.canvas.print_figure(bytes_io, **kw)


# we have some minimum values that are equal to zero. lets keep that in time
# Start understanding patterns - correlation matrix
correlation_matrix = df.corr()
plt.figure(figsize=(20,15))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=.5)
plt.show()


# let me select 12 stocks from various industries, in some cases multiple stocks from the same industry
# I want to see what kind of results PCA gives with correlated and uncorrelated stocks
# Moving correlation - stock selection
stock_names = ['GARAN', 'AKBNK', 'YKBNK', 'TCELL', 'TTKOM', 'KRDMD', 'EREGL', 'PETKM', 'TUPRS', 'VESTL']
selected_stocks = df[stock_names]
print(selected_stocks)

        GARAN   AKBNK   YKBNK    TCELL   TTKOM   KRDMD   EREGL   PETKM  \
0      6.3715  5.2084  2.5438   4.5359  4.2639  0.7165  0.7914  0.7866   
1      6.3386  5.1938  2.5266   4.5153  4.2521  0.7165  0.7844  0.7829   
2      6.3386  5.2084  2.5266   4.5153  4.2521  0.7165  0.7914  0.7903   
3      6.3715  5.1938  2.5324   4.5359  4.2521  0.7104  0.7914  0.7903   
4      6.3715  5.2084  2.5324   4.5153  4.2521  0.7104  0.7914  0.7941   
...       ...     ...     ...      ...     ...     ...     ...     ...   
50007  9.8500  7.7300  2.7500  13.4500  5.6000  2.2800  7.6900  4.1000   
50008  9.8600  7.7200  2.7500  13.4300  5.5700  2.2800  7.6500  4.1000   
50009  9.8600  7.7400  2.7600  13.4600  5.5700  2.2800  7.6700  4.1000   
50010  9.8400  7.7000  2.7700  13.4300  5.5700  2.2800  7.6700  4.0900   
50011  9.8300  7.7000  2.7700  13.4000  5.5600  2.2700  7.6700  4.0900   

          TUPRS  VESTL  
0       29.8072   1.90  
1       29.7393   1.90  
2       29.6716   1.91  
3       29.7393   1.91  
4       29.8072   1.90  
...         ...    ...  
50007  131.6000   9.98  
50008  131.5000   9.98  
50009  131.5000   9.96  
50010  131.3000   9.94  
50011  131.8000   9.93  

[50012 rows x 10 columns]


# Moving correlation - window selection - 7 hours in a day, 22 days in a month = 616 ~ used 600
window_size = 600
# Moving correlation - calculation
rolling_correlations = selected_stocks.rolling(window=window_size).corr(pairwise=True)
# Moving correlation - three point in time, beginning, some middle point and end
time_points = rolling_correlations.index.get_level_values(0).unique()
beginning = time_points[window_size*3]
middle = time_points[len(unique_time_points) // 2]
end = time_points[-1]

def plot_heatmap(time_point):
    corr_matrix = rolling_correlations.xs(time_point, level=0)
    plt.figure(figsize=(10, 8))
    sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", vmin=-1, vmax=1)
    plt.title(f"Correlations at time point {time_point}")
    plt.show()

plot_heatmap(beginning) 
plot_heatmap(middle)  
plot_heatmap(end)


# For the stocks in the same industry correlation varied however it is limited.
# For example look at (Garan, Akbnk, Ykbnk) or (Krdmd, Eregl)
# For the stocks not in the same industry correlation varied significantly
# For example look at (Tuprs, Asels)
# Now I am curious to see a timeseries to see changes over time
# garan and akbnk combination over time:
rolling_corr_garan_akbnk = rolling_correlations.loc[pd.IndexSlice[:, 'GARAN'], 'AKBNK']
plt.figure(figsize=(14,7))
rolling_corr_garan_akbnk.plot()
plt.title("Rolling Correlation Between GARAN and AKBNK")
plt.xlabel("Date")
plt.ylabel("Correlation")
plt.grid(True)
plt.tight_layout()
plt.show()
# bagfs and gubrf combination over time:
rolling_corr_krdmd_eregl = rolling_correlations.loc[pd.IndexSlice[:, 'KRDMD'], 'EREGL']
plt.figure(figsize=(14,7))
rolling_corr_krdmd_eregl.plot()
plt.title("Rolling Correlation Between KRDMD and EREGL")
plt.xlabel("Date")
plt.ylabel("Correlation")
plt.grid(True)
plt.tight_layout()
plt.show()
# tuprs and asels combination over time:
rolling_corr_tuprs_vestl = rolling_correlations.loc[pd.IndexSlice[:, 'TUPRS'], 'VESTL']
plt.figure(figsize=(14,7))
rolling_corr_tuprs_vestl.plot()
plt.title("Rolling Correlation Between TUPRS and VESTL")
plt.xlabel("Date")
plt.ylabel("Correlation")
plt.grid(True)
plt.tight_layout()
plt.show()


# correlation in banking is a bit tighter
# correlation in industrial is a bit loose
# still we see extreme highs and lows for stocks, even if they are in the same industry
# somehow we need to make data stationary.
# let me take time difference of stocks to ensure stationarity and do the analysis above again
differenced_stocks = selected_stocks.diff().dropna()
rolling_correlations = differenced_stocks.rolling(window=window_size).corr(pairwise=True)
plot_heatmap(beginning) 
plot_heatmap(middle)  
plot_heatmap(end)


# let me look at time series to check stationarity
# garan and akbnk combination over time: 
rolling_corr_garan_akbnk = rolling_correlations.loc[pd.IndexSlice[:, 'GARAN'], 'AKBNK']
plt.figure(figsize=(14,7))
rolling_corr_garan_akbnk.plot()
plt.title("Rolling Correlation Between GARAN and AKBNK")
plt.xlabel("Date")
plt.ylabel("Correlation")
plt.grid(True)
plt.tight_layout()
plt.show()
# krdmd and eregl combination over time:
rolling_corr_krdmd_eregl = rolling_correlations.loc[pd.IndexSlice[:, 'KRDMD'], 'EREGL']
plt.figure(figsize=(14,7))
rolling_corr_krdmd_eregl.plot()
plt.title("Rolling Correlation Between KRDMD and EREGL")
plt.xlabel("Date")
plt.ylabel("Correlation")
plt.grid(True)
plt.tight_layout()
plt.show()
# tuprs and vestl combination over time:
rolling_corr_tuprs_vestl = rolling_correlations.loc[pd.IndexSlice[:, 'TUPRS'], 'VESTL']
plt.figure(figsize=(14,7))
rolling_corr_tuprs_vestl.plot()
plt.title("Rolling Correlation Between TUPRS and VESTL")
plt.xlabel("Date")
plt.ylabel("Correlation")
plt.grid(True)
plt.tight_layout()
plt.show()


# Looks more stationary now
# Calculate correlation matrix
correlation_matrix = differenced_stocks.corr()
display(correlation_matrix)


# Calculate correlation matrix
pca = PCA()
pca.fit(correlation_matrix)
explained_variance = pca.explained_variance_ratio_
plt.figure(figsize=(10, 6))
plt.bar(range(1, len(explained_variance) + 1), explained_variance, alpha=0.5, align='center', label='Individual explained variance')
plt.ylabel('Explained variance ratio')
plt.xlabel('Principal components')
plt.legend(loc='best')
plt.tight_layout()
plt.show()
plt.figure(figsize=(10, 6))
plt.step(range(1, len(explained_variance) + 1), explained_variance.cumsum(), where='mid', label='Cumulative explained variance')
plt.ylabel('Cumulative explained variance ratio')
plt.xlabel('Principal components')
plt.legend(loc='best')
plt.tight_layout()
plt.show()


# Aiming to capture 95% of the variance so going to move with 5 components
components = 5
pca_opt = PCA(components)
principal_components = pca_opt.fit_transform(correlation_matrix)
pc_df = pd.DataFrame(data=principal_components, columns=[f"Principal Component {i}" for i in range(1, components + 1)])
explained_variance = pca_opt.explained_variance_ratio_
print(f"Explained variance for each component: {explained_variance}")
print(pc_df)

Explained variance for each component: [0.63412451 0.14506967 0.07051866 0.05570385 0.05113044]
   Principal Component 1  Principal Component 2  Principal Component 3  \
0              -0.446413               0.013542               0.003487   
1              -0.448004               0.013773              -0.006821   
2              -0.432381               0.020839              -0.022524   
3              -0.182885              -0.004732              -0.130479   
4              -0.379656               0.018568              -0.047819   
5               0.137701              -0.130967               0.241272   
6               0.899229              -0.408735              -0.143855   
7               0.144749              -0.061917               0.323292   
8              -0.047919              -0.017699              -0.198764   
9               0.755579               0.557328              -0.017789   

   Principal Component 4  Principal Component 5  
0              -0.053235              -0.033266  
1              -0.050514              -0.036664  
2              -0.047332              -0.034605  
3              -0.037181              -0.132515  
4              -0.034669              -0.037627  
5              -0.199368               0.250654  
6              -0.055692              -0.089322  
7               0.259676              -0.132138  
8               0.263635               0.255519  
9              -0.045321              -0.010035


# PCA 1 looks like seperation of banking and telecommunication sector from the rest and especially from technology
# PCA 2 looks like seperation of iron and steel sector from the rest and especially from technology
# PCA 4 looks like separation of energy sector from the rest


# read google trends data from anaconda cloud, just first 10 rows to see if it works
# if I download more than 12 months of trends data, google trends provide the data in months, that is why I am limiting the period with 12 months
# maybe later I can pull the data through API
file_name = 'multiTimeline (3).csv'
trends = pd.read_csv(file_name, delimiter=',', skiprows=1)
trends['Week'] = pd.to_datetime(trends['Week'])
display(trends.head())


# slices tupras yet received an error saying df might be affected, so created a copy for the slice then merged with google trends data
tupras = df[['timestamp', 'TUPRS']].copy()
tupras['timestamp'] = tupras['timestamp'].dt.tz_localize(None)
tupras.set_index('timestamp', inplace=True)
weekly_avg_tuprs = tupras.resample('W').mean().reset_index()
merged_tupras = weekly_avg_tuprs.merge(trends, left_on='timestamp', right_on='Week', how='inner')
display(merged_tupras)


# adding a price change tracker condition: stock price increases more than 2.5% and any of trend item increases more than 2.5%
# scaling trends data between 0 - 100 just like trends data then plotting
scaler = MinMaxScaler(feature_range=(0, 100))

def check_pct_change(df, column_name):
    return df[column_name].pct_change() > 0.05

trend_columns = ['tüpraş: (Türkiye)', 'tüpraş hisse: (Türkiye)', 'tüpraş borsa: (Türkiye)', 'IST TUPRS: (Türkiye)', 'TUPRS: (Türkiye)']

merged_tupras['price_increase'] = merged_tupras['TUPRS'].pct_change() > 0.05

for column in trend_columns:
    merged_tupras[f'{column}_increase'] = check_pct_change(merged_tupras, column)

merged_tupras['any_trend_increase'] = merged_tupras[[f'{column}_increase' for column in trend_columns]].any(axis=1)

merged_tupras['highlight_condition'] = merged_tupras['price_increase'] & merged_tupras['any_trend_increase']
merged_tupras['TUPRS_scaled'] = scaler.fit_transform(merged_tupras[['TUPRS']])

plt.figure(figsize=(15, 7))
plt.plot(merged_tupras['timestamp'], merged_tupras['TUPRS_scaled'], label='Normalized TUPRS Stock Prices', color='blue')
plt.plot(merged_tupras['timestamp'], merged_tupras['tüpraş: (Türkiye)'], label='tüpraş', color='red')
plt.plot(merged_tupras['timestamp'], merged_tupras['tüpraş hisse: (Türkiye)'], label='tüpraş hisse', color='green')
plt.plot(merged_tupras['timestamp'], merged_tupras['tüpraş borsa: (Türkiye)'], label='tüpraş borsa', color='yellow')
plt.plot(merged_tupras['timestamp'], merged_tupras['IST TUPRS: (Türkiye)'], label='IST TUPRS', color='purple')
plt.plot(merged_tupras['timestamp'], merged_tupras['TUPRS: (Türkiye)'], label='TUPRS', color='orange') 

for i, row in merged_tupras.iterrows():
    if row['price_increase']:
        plt.axvline(x=row['timestamp'], color='grey', linestyle='--')

plt.title('Normalized TUPRS Stock Prices vs. Google Trends Data')
plt.xlabel('Time')
plt.ylabel('Normalized Value')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()


parsed_merged_tupras = merged_tupras.drop('Week', axis=1)
parsed_merged_tupras = parsed_merged_tupras.drop('timestamp', axis=1)
trends_correlations = parsed_merged_tupras.rolling(window=6).corr(pairwise=True)
display(trends_correlations)


trends_correlation_TUPRS_tüpraş = trends_correlations.loc[pd.IndexSlice[:, 'TUPRS'], 'tüpraş: (Türkiye)']
plt.figure(figsize=(14,7))
trends_correlation_TUPRS_tüpraş.plot()
plt.title("Rolling Correlation Between TUPRS and tüpraş")
plt.xlabel("Date")
plt.ylabel("Correlation")
plt.grid(True)
plt.tight_layout()
plt.show()

	timestamp	AEFES	AKBNK	AKSA	AKSEN	ALARK	ALBRK	ANACM	ARCLK	ASELS	ASUZU	AYGAZ	BAGFS	BANVT	BRISA	CCOLA	CEMAS	ECILC	EREGL	FROTO	GARAN	GOODY	GUBRF	HALKB	ICBCT	ISCTR	ISDMR	ISFIN	ISYAT	KAREL	KARSN	KCHOL	KRDMB	KRDMD	MGROS	OTKAR	PARSN	PETKM	PGSUS	PRKME	SAHOL	SASA	SISE	SKBNK	SODA	TCELL	THYAO	TKFEN	TOASO	TRKCM	TSKB	TTKOM	TUKAS	TUPRS	USAK	VAKBN	VESTL	YATAS	YKBNK	YUNSA	ZOREN
0	2012-09-17T06:45:00Z	22.3978	5.2084	1.7102	3.87	1.4683	1.1356	1.0634	6.9909	2.9948	2.4998	3.5396	37.4875	3.95	3.9666	27.2590	1.20	0.8046	0.7914	12.0559	6.3715	1.8479	2.7434	12.9606	0.72	4.0807	NaN	0.2450	0.2065	1.8469	1.0818	6.0642	1.3738	0.7165	19.60	24.1387	2.86	0.7866	NaN	4.3021	6.8172	0.3002	0.9932	1.1651	0.3178	4.5359	3.3661	4.8172	5.5142	0.4344	0.8294	4.2639	0.96	29.8072	1.0382	3.8620	1.90	0.4172	2.5438	2.2619	0.7789
1	2012-09-17T07:00:00Z	22.3978	5.1938	1.7066	3.86	1.4574	1.1275	1.0634	6.9259	2.9948	2.5100	3.5483	37.2769	3.96	3.9666	27.3471	1.19	0.8088	0.7844	11.9592	6.3386	1.8479	2.7541	12.8742	0.72	4.0519	NaN	0.2450	0.2065	1.8237	1.0818	6.0475	1.3608	0.7165	19.35	24.1387	2.84	0.7829	NaN	4.2892	6.7831	0.2955	0.9897	1.1651	0.3178	4.5153	3.3574	4.8172	5.5142	0.4344	0.8294	4.2521	0.96	29.7393	1.0382	3.8529	1.90	0.4229	2.5266	2.2462	0.7789
2	2012-09-17T07:15:00Z	22.3978	5.2084	1.7102	NaN	1.4610	1.1356	1.0679	6.9909	2.9855	2.4796	3.5483	37.2769	3.96	3.9356	27.3471	1.19	0.8088	0.7914	11.8950	6.3386	1.8479	2.7541	12.9174	0.72	4.0519	NaN	0.2422	0.2065	1.8237	1.0726	6.0475	1.3608	0.7165	19.40	24.2013	2.85	0.7903	NaN	4.2892	6.7831	0.2955	0.9932	1.1561	0.3178	4.5153	3.3661	4.8317	5.5142	0.4364	0.8294	4.2521	0.97	29.6716	1.0463	3.8436	1.91	0.4229	2.5266	2.2566	0.7789
3	2012-09-17T07:30:00Z	22.3978	5.1938	1.7102	3.86	1.4537	1.1275	1.0679	6.9584	2.9855	2.4897	3.5396	37.3822	3.95	3.9666	27.4347	1.20	0.8088	0.7914	11.9592	6.3715	1.8648	2.7541	12.9606	0.72	4.0663	NaN	NaN	NaN	1.8391	1.0726	6.0475	1.3673	0.7104	19.45	24.3888	2.86	0.7903	NaN	4.2763	6.7831	0.2955	0.9897	1.1561	0.3168	4.5359	3.3748	4.8172	5.5142	0.4364	0.8253	4.2521	0.97	29.7393	1.0382	3.8529	1.91	0.4286	2.5324	2.2619	0.7860
4	2012-09-17T07:45:00Z	22.5649	5.2084	1.7102	3.87	1.4574	1.1356	1.0725	6.9909	2.9760	2.4897	3.5396	37.4875	3.96	3.9666	27.4347	1.20	0.8130	0.7914	11.9914	6.3715	1.8648	2.7541	12.9606	0.73	4.0807	NaN	0.2422	0.2086	1.8314	1.0726	6.0309	1.3608	0.7104	19.40	24.3888	2.85	0.7941	NaN	4.2763	6.7831	0.2979	0.9970	1.1651	0.3178	4.5153	3.3748	4.8317	5.5264	0.4404	0.8253	4.2521	0.97	29.8072	1.0382	3.8620	1.90	0.4286	2.5324	2.2619	0.7789
5	2012-09-17T08:00:00Z	22.5649	5.2229	1.7102	3.86	1.4610	1.1275	1.0725	6.9584	2.9760	2.4897	3.5396	37.3822	3.95	3.9666	27.4347	1.20	0.8172	0.7914	12.0234	6.3715	1.8648	2.7646	12.9606	NaN	4.0807	NaN	0.2450	0.2065	1.8391	1.0726	6.0143	1.3673	0.7042	19.45	24.3888	2.84	0.7903	NaN	4.2633	6.8002	0.2979	0.9970	1.1651	0.3189	4.5359	3.3748	4.8463	5.5383	0.4404	0.8294	4.2402	NaN	29.8072	1.0382	3.8620	1.91	0.4314	2.5381	2.2566	0.7860
6	2012-09-17T08:15:00Z	22.5649	5.2229	1.7066	NaN	1.4610	1.1275	1.0679	6.9584	2.9760	2.4998	3.5483	37.2769	3.96	3.9821	27.3471	1.20	0.8172	0.7914	12.0559	6.3715	1.8648	2.7646	12.9606	0.73	4.0663	NaN	0.2450	NaN	1.8314	1.0726	6.0143	1.3608	0.7104	19.45	24.5137	NaN	0.7903	NaN	4.2633	6.8002	0.2979	0.9932	1.1651	0.3189	4.5359	3.3748	4.8463	5.5264	0.4384	0.8253	4.2402	0.97	29.6716	1.0382	3.8529	1.91	0.4286	2.5324	2.2566	0.7789
7	2012-09-17T08:30:00Z	22.5649	5.2084	1.7066	3.86	1.4610	NaN	1.0725	6.9909	2.9855	2.4897	3.5483	37.1716	3.96	3.9977	27.1710	1.20	0.8172	0.7879	12.0234	6.3386	1.8817	2.7541	12.9174	0.72	4.0519	NaN	NaN	NaN	NaN	1.0726	6.0143	1.3608	0.7104	19.40	24.4515	2.84	0.7903	NaN	4.2633	6.8002	0.2979	0.9932	1.1651	0.3189	4.5359	3.3661	4.8463	5.5383	0.4384	0.8253	4.2168	0.97	29.7393	1.0463	3.8529	1.91	0.4286	2.5266	2.2566	0.7860
8	2012-09-17T08:45:00Z	22.5649	5.2372	1.6995	NaN	1.4610	1.1275	1.0725	6.9909	2.9855	2.4998	3.5396	37.1716	3.95	3.9821	27.2590	1.20	0.8130	0.7914	12.0234	6.3715	1.8817	2.7541	12.9606	0.72	4.0807	NaN	0.2450	0.2086	1.8469	1.0636	6.0309	1.3673	0.7104	19.45	24.3888	2.85	0.7903	NaN	4.2633	6.8002	0.2979	0.9932	1.1561	0.3189	4.5979	3.3748	4.8608	5.5383	0.4384	0.8212	4.2285	0.96	29.7393	1.0382	3.8620	1.90	0.4314	2.5381	2.2514	0.7789
9	2012-09-17T09:00:00Z	22.5649	5.2372	1.6995	3.86	1.4610	1.1356	1.0725	6.9909	2.9855	2.4897	3.5483	37.2769	3.95	3.9666	27.2590	1.20	0.8130	0.7879	11.9914	6.3715	1.8817	2.7434	13.0039	0.72	4.0807	NaN	0.2450	0.2065	1.8391	1.0726	6.0309	1.3673	0.7104	19.50	24.3888	NaN	0.7903	NaN	4.2503	6.8002	0.2979	0.9932	1.1651	0.3178	4.5979	3.3748	4.8608	5.5383	0.4384	0.8253	4.2285	0.97	29.7393	1.0382	3.8620	1.91	0.4314	2.5324	2.2619	0.7789

	timestamp	AEFES	AKBNK	AKSA	AKSEN	ALARK	ALBRK	ANACM	ARCLK	ASELS	ASUZU	AYGAZ	BAGFS	BANVT	BRISA	CCOLA	CEMAS	ECILC	EREGL	FROTO	GARAN	GOODY	GUBRF	HALKB	ICBCT	ISCTR	ISDMR	ISFIN	ISYAT	KAREL	KARSN	KCHOL	KRDMB	KRDMD	MGROS	OTKAR	PARSN	PETKM	PGSUS	PRKME	SAHOL	SASA	SISE	SKBNK	SODA	TCELL	THYAO	TKFEN	TOASO	TRKCM	TSKB	TTKOM	TUKAS	TUPRS	USAK	VAKBN	VESTL	YATAS	YKBNK	YUNSA	ZOREN
0	2012-09-17T06:45:00Z	22.3978	5.2084	1.7102	3.87	1.4683	1.1356	1.0634	6.9909	2.9948	2.4998	3.5396	37.4875	3.95	3.9666	27.2590	1.20	0.8046	0.7914	12.0559	6.3715	1.8479	2.7434	12.9606	0.72	4.0807	NaN	0.2450	0.2065	1.8469	1.0818	6.0642	1.3738	0.7165	19.60	24.1387	2.86	0.7866	NaN	4.3021	6.8172	0.3002	0.9932	1.1651	0.3178	4.5359	3.3661	4.8172	5.5142	0.4344	0.8294	4.2639	0.96	29.8072	1.0382	3.8620	1.90	0.4172	2.5438	2.2619	0.7789
1	2012-09-17T07:00:00Z	22.3978	5.1938	1.7066	3.86	1.4574	1.1275	1.0634	6.9259	2.9948	2.5100	3.5483	37.2769	3.96	3.9666	27.3471	1.19	0.8088	0.7844	11.9592	6.3386	1.8479	2.7541	12.8742	0.72	4.0519	NaN	0.2450	0.2065	1.8237	1.0818	6.0475	1.3608	0.7165	19.35	24.1387	2.84	0.7829	NaN	4.2892	6.7831	0.2955	0.9897	1.1651	0.3178	4.5153	3.3574	4.8172	5.5142	0.4344	0.8294	4.2521	0.96	29.7393	1.0382	3.8529	1.90	0.4229	2.5266	2.2462	0.7789
2	2012-09-17T07:15:00Z	22.3978	5.2084	1.7102	NaN	1.4610	1.1356	1.0679	6.9909	2.9855	2.4796	3.5483	37.2769	3.96	3.9356	27.3471	1.19	0.8088	0.7914	11.8950	6.3386	1.8479	2.7541	12.9174	0.72	4.0519	NaN	0.2422	0.2065	1.8237	1.0726	6.0475	1.3608	0.7165	19.40	24.2013	2.85	0.7903	NaN	4.2892	6.7831	0.2955	0.9932	1.1561	0.3178	4.5153	3.3661	4.8317	5.5142	0.4364	0.8294	4.2521	0.97	29.6716	1.0463	3.8436	1.91	0.4229	2.5266	2.2566	0.7789
3	2012-09-17T07:30:00Z	22.3978	5.1938	1.7102	3.86	1.4537	1.1275	1.0679	6.9584	2.9855	2.4897	3.5396	37.3822	3.95	3.9666	27.4347	1.20	0.8088	0.7914	11.9592	6.3715	1.8648	2.7541	12.9606	0.72	4.0663	NaN	NaN	NaN	1.8391	1.0726	6.0475	1.3673	0.7104	19.45	24.3888	2.86	0.7903	NaN	4.2763	6.7831	0.2955	0.9897	1.1561	0.3168	4.5359	3.3748	4.8172	5.5142	0.4364	0.8253	4.2521	0.97	29.7393	1.0382	3.8529	1.91	0.4286	2.5324	2.2619	0.7860
4	2012-09-17T07:45:00Z	22.5649	5.2084	1.7102	3.87	1.4574	1.1356	1.0725	6.9909	2.9760	2.4897	3.5396	37.4875	3.96	3.9666	27.4347	1.20	0.8130	0.7914	11.9914	6.3715	1.8648	2.7541	12.9606	0.73	4.0807	NaN	0.2422	0.2086	1.8314	1.0726	6.0309	1.3608	0.7104	19.40	24.3888	2.85	0.7941	NaN	4.2763	6.7831	0.2979	0.9970	1.1651	0.3178	4.5153	3.3748	4.8317	5.5264	0.4404	0.8253	4.2521	0.97	29.8072	1.0382	3.8620	1.90	0.4286	2.5324	2.2619	0.7789

	timestamp	AEFES	AKBNK	AKSA	AKSEN	ALARK	ALBRK	ANACM	ARCLK	ASELS	ASUZU	AYGAZ	BAGFS	BANVT	BRISA	CCOLA	CEMAS	ECILC	EREGL	FROTO	GARAN	GOODY	GUBRF	HALKB	ICBCT	ISCTR	ISDMR	ISFIN	ISYAT	KAREL	KARSN	KCHOL	KRDMB	KRDMD	MGROS	OTKAR	PARSN	PETKM	PGSUS	PRKME	SAHOL	SASA	SISE	SKBNK	SODA	TCELL	THYAO	TKFEN	TOASO	TRKCM	TSKB	TTKOM	TUKAS	TUPRS	USAK	VAKBN	VESTL	YATAS	YKBNK	YUNSA	ZOREN
50007	2019-07-23T14:00:00Z	20.48	7.73	9.14	2.47	3.23	1.21	2.84	20.30	NaN	8.13	9.84	6.95	16.04	5.89	33.80	0.72	2.43	7.69	62.50	9.85	2.60	3.03	6.36	3.56	6.33	6.76	2.59	1.12	NaN	NaN	19.10	2.13	2.28	15.27	122.3	14.18	4.10	48.84	2.81	9.44	NaN	NaN	0.99	6.39	13.45	13.06	25.64	20.26	NaN	0.85	5.60	4.34	131.6	1.05	4.86	9.98	5.35	2.75	4.25	NaN
50008	2019-07-23T14:15:00Z	20.50	7.72	9.14	2.47	3.22	1.21	2.84	20.32	NaN	8.04	9.89	6.99	15.89	5.88	33.58	0.73	2.42	7.65	62.55	9.86	2.59	3.02	6.35	3.55	6.30	6.77	2.59	1.12	NaN	NaN	19.11	2.13	2.28	15.28	122.1	14.18	4.10	48.62	2.82	9.41	NaN	NaN	0.99	6.39	13.43	13.06	25.64	20.18	NaN	0.84	5.57	4.35	131.5	1.05	4.86	9.98	5.34	2.75	4.24	NaN
50009	2019-07-23T14:30:00Z	20.50	7.74	9.13	2.46	3.23	1.21	2.83	20.34	NaN	8.09	9.89	6.98	15.95	5.89	33.80	0.73	2.42	7.67	62.65	9.86	2.59	3.03	6.35	3.56	6.33	6.79	2.59	1.15	NaN	NaN	19.14	2.14	2.28	15.26	122.7	14.18	4.10	48.62	2.82	9.41	NaN	NaN	0.98	6.39	13.46	13.07	25.62	20.20	NaN	0.85	5.57	4.36	131.5	1.05	4.86	9.96	5.34	2.76	4.24	NaN
50010	2019-07-23T14:45:00Z	20.40	7.70	9.14	2.47	3.24	1.21	2.82	20.38	NaN	7.98	9.82	6.97	16.03	5.89	33.52	0.73	2.42	7.67	62.55	9.84	2.58	3.06	6.33	3.55	6.31	6.79	2.60	1.14	NaN	NaN	19.15	2.13	2.28	15.25	123.2	14.19	4.09	48.62	2.81	9.42	NaN	NaN	0.99	6.37	13.43	13.05	25.52	20.26	NaN	0.85	5.57	4.35	131.3	1.04	4.86	9.94	5.34	2.77	4.24	NaN
50011	2019-07-23T15:00:00Z	20.46	7.70	9.14	2.47	3.23	1.20	2.83	20.32	NaN	7.97	9.82	6.98	16.11	5.87	33.80	0.72	2.42	7.67	62.50	9.83	2.59	3.06	6.34	3.54	6.31	6.77	2.60	1.14	NaN	NaN	19.13	2.13	2.27	15.23	123.2	14.19	4.09	48.72	2.82	9.46	NaN	NaN	0.98	6.37	13.40	13.04	25.50	20.20	NaN	0.85	5.56	4.34	131.8	1.05	4.85	9.93	5.33	2.77	4.24	NaN

	AEFES	AKBNK	AKSA	AKSEN	ALARK	ALBRK	ANACM	ARCLK	ASELS	ASUZU	AYGAZ	BAGFS	BANVT	BRISA	CCOLA	CEMAS	ECILC	EREGL	FROTO	GARAN	GOODY	GUBRF	HALKB	ICBCT	ISCTR	ISDMR	ISFIN	ISYAT	KAREL	KARSN	KCHOL	KRDMB	KRDMD	MGROS	OTKAR	PARSN	PETKM	PGSUS	PRKME	SAHOL	SASA	SISE	SKBNK	SODA	TCELL	THYAO	TKFEN	TOASO	TRKCM	TSKB	TTKOM	TUKAS	TUPRS	USAK	VAKBN	VESTL	YATAS	YKBNK	YUNSA	ZOREN
count	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	26057.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	46015.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000	50012.000000
mean	20.999765	6.473033	7.098991	3.198649	2.058960	1.366489	1.665100	15.374719	13.521743	6.450174	8.066140	10.392947	7.585731	6.546420	36.879707	1.220327	2.064860	4.190049	32.753425	7.902257	3.101909	4.326464	10.916680	2.727475	5.129005	3.867774	1.457677	0.525442	3.088948	1.324861	12.247970	2.207865	1.769405	19.577136	81.128430	7.994128	2.541587	24.806137	2.933022	8.614563	2.249260	3.051637	1.478175	3.151714	9.831372	9.302941	9.195998	16.558826	2.025407	0.945122	5.658767	1.706822	63.064551	1.213295	4.735101	5.917606	2.313140	2.565365	4.045200	1.246740
std	2.487804	0.944306	2.721361	0.735249	0.573195	0.167572	0.785623	4.533101	9.618629	2.192312	2.608643	3.604752	6.240489	1.294799	6.735998	0.799952	0.973493	2.693558	14.762311	1.249090	0.892034	1.224316	3.070512	1.741387	1.004176	2.038660	1.683149	0.158571	2.104433	0.289648	3.182249	0.688680	0.940351	3.898629	27.990962	4.650927	1.378543	7.649953	0.723832	0.954221	2.480564	1.425571	0.294379	2.058252	2.359280	4.030324	6.676604	6.338753	1.101318	0.154439	0.819881	0.859530	32.441403	0.457364	0.977634	2.842267	2.522167	0.423269	1.328115	0.310956
min	0.000100	0.000100	0.000100	0.000000	0.000100	1.025500	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000000	0.000100	0.000100	0.000000	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000000	0.000100	1.018100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000000	0.000100	0.000000	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.000100	0.650000	0.000100	0.000100	0.000100	0.000000	0.000100	0.000100	0.000100	0.000100
25%	19.178300	5.850000	5.183100	2.680000	1.569200	1.230000	1.047000	11.711100	4.986900	5.067400	5.922600	8.224100	2.580000	5.893800	31.978200	0.700000	1.166200	2.201800	21.417900	7.020000	2.427300	3.276500	8.710900	1.540000	4.321900	1.804737	0.563900	0.436800	1.522000	1.104500	9.736800	1.542400	1.084500	16.670000	56.557300	4.490000	1.286900	17.800000	2.390000	7.965200	0.314400	1.922000	1.200000	1.436200	8.566300	6.436400	4.327500	10.327200	1.169400	0.825400	5.265900	1.030000	34.549100	0.949000	4.030000	3.950000	0.340000	2.268200	2.882700	1.026800
50%	20.671000	6.302300	6.978400	2.940000	1.937063	1.363300	1.254900	14.986900	9.304250	5.930000	7.695400	10.610000	3.700000	6.730000	34.840300	0.890000	1.803600	3.037900	27.108500	7.659900	3.186100	4.247800	10.655900	2.000000	4.860000	3.170637	0.732400	0.483400	1.800500	1.280000	12.035600	2.181300	1.397900	19.140000	82.543000	7.710000	2.285900	25.650000	2.745200	8.607900	0.640600	2.668200	1.520000	2.652400	9.700100	7.780000	5.736300	16.506400	1.625900	0.937400	5.740000	1.520000	49.554200	1.046300	4.480000	6.300000	0.905800	2.609300	4.084800	1.250000
75%	22.732000	6.931900	8.690000	3.790000	2.409500	1.500900	2.387400	19.068900	22.816600	7.090000	10.225100	12.340000	11.910000	7.330000	42.004100	1.530000	2.763200	6.806000	48.556500	8.680000	3.595700	5.124900	13.490900	3.711250	5.826500	5.920000	1.384300	0.613300	5.150000	1.470000	15.169300	2.722500	2.169000	22.100000	105.300000	10.510000	3.882800	29.440000	3.462300	9.265200	4.845900	4.155400	1.720700	4.277900	11.245200	12.300000	14.258400	20.614600	2.982600	1.024400	6.260000	2.110000	93.496650	1.354600	5.240700	7.440000	4.060000	2.874000	4.672800	1.426500
max	28.509000	9.212400	15.118900	5.190000	3.514300	2.190000	3.502100	26.427800	46.761600	15.280000	13.593500	38.435200	28.680000	10.327500	54.220800	7.010000	4.227800	10.471000	65.419200	12.155400	58.757400	13.619100	20.236500	11.270000	7.963900	7.593600	9.830000	1.150000	9.460000	2.500000	19.150000	4.496000	4.951000	30.260000	139.428800	29.820000	5.769700	50.650000	5.430000	11.682600	8.426000	6.923000	2.251600	7.765900	15.812500	19.950000	27.320000	29.921800	4.643200	1.420800	7.350000	5.920000	139.293700	2.757800	7.581400	14.540000	10.674800	3.958100	9.527500	2.443000

	GARAN	AKBNK	YKBNK	TCELL	TTKOM	KRDMD	EREGL	PETKM	TUPRS	VESTL
GARAN	1.000000	0.949921	0.927242	0.764507	0.876966	0.666391	0.396921	0.655919	0.705607	0.451847
AKBNK	0.949921	1.000000	0.926040	0.766739	0.879081	0.656685	0.390477	0.648716	0.704287	0.443262
YKBNK	0.927242	0.926040	1.000000	0.755685	0.872589	0.638565	0.368674	0.627574	0.693180	0.425791
TCELL	0.764507	0.766739	0.755685	1.000000	0.739447	0.549194	0.364994	0.549361	0.599329	0.371744
TTKOM	0.876966	0.879081	0.872589	0.739447	1.000000	0.610872	0.357068	0.603661	0.675198	0.403853
KRDMD	0.666391	0.656685	0.638565	0.549194	0.610872	1.000000	0.455818	0.569310	0.533062	0.393900
EREGL	0.396921	0.390477	0.368674	0.364994	0.357068	0.455818	1.000000	0.420360	0.365844	0.305980
PETKM	0.655919	0.648716	0.627574	0.549361	0.603661	0.569310	0.420360	1.000000	0.531551	0.402220
TUPRS	0.705607	0.704287	0.693180	0.599329	0.675198	0.533062	0.365844	0.531551	1.000000	0.364741
VESTL	0.451847	0.443262	0.425791	0.371744	0.403853	0.393900	0.305980	0.402220	0.364741	1.000000

	Week	tüpraş: (Türkiye)	tüpraş hisse: (Türkiye)	tüpraş borsa: (Türkiye)	IST TUPRS: (Türkiye)	TUPRS: (Türkiye)
0	2018-07-29	62	12	6	0	10
1	2018-08-05	54	21	5	0	20
2	2018-08-12	65	21	4	1	17
3	2018-08-19	43	3	0	0	4
4	2018-08-26	82	8	3	3	11

	timestamp	TUPRS	Week	tüpraş: (Türkiye)	tüpraş hisse: (Türkiye)	tüpraş borsa: (Türkiye)	IST TUPRS: (Türkiye)	TUPRS: (Türkiye)
0	2018-07-29	93.790785	2018-07-29	62	12	6	0	10
1	2018-08-05	96.481615	2018-08-05	54	21	5	0	20
2	2018-08-12	94.438693	2018-08-12	65	21	4	1	17
3	2018-08-19	107.122541	2018-08-19	43	3	0	0	4
4	2018-08-26	109.350308	2018-08-26	82	8	3	3	11
5	2018-09-02	107.887585	2018-09-02	70	13	1	0	6
6	2018-09-09	109.635110	2018-09-09	66	14	3	0	12
7	2018-09-16	114.940775	2018-09-16	57	12	1	1	6
8	2018-09-23	117.481858	2018-09-23	54	16	2	1	8
9	2018-09-30	121.099080	2018-09-30	49	13	3	0	11
10	2018-10-07	119.705243	2018-10-07	70	14	1	1	13
11	2018-10-14	123.710780	2018-10-14	69	12	0	0	13
12	2018-10-21	124.264020	2018-10-21	52	9	1	3	14
13	2018-10-28	118.523692	2018-10-28	52	15	1	1	10
14	2018-11-04	114.754028	2018-11-04	75	18	2	2	21
15	2018-11-11	117.572665	2018-11-11	51	15	4	1	12
16	2018-11-18	108.780285	2018-11-18	39	13	1	0	11
17	2018-11-25	109.358799	2018-11-25	43	12	2	0	12
18	2018-12-02	109.475294	2018-12-02	41	11	3	0	7
19	2018-12-09	112.483309	2018-12-09	34	7	1	1	7
20	2018-12-16	109.822307	2018-12-16	45	12	1	0	9
21	2018-12-23	109.262689	2018-12-23	39	11	1	0	13
22	2018-12-30	104.310698	2018-12-30	27	6	1	0	8
23	2019-01-06	103.277273	2019-01-06	37	11	1	3	7
24	2019-01-13	106.569909	2019-01-13	43	15	3	0	8
25	2019-01-20	109.814765	2019-01-20	43	17	2	0	8
26	2019-01-27	119.207547	2019-01-27	43	14	2	0	8
27	2019-02-03	123.117458	2019-02-03	61	14	4	2	7
28	2019-02-10	123.924902	2019-02-10	62	17	2	0	4
29	2019-02-17	128.500774	2019-02-17	52	15	1	1	7
30	2019-02-24	127.989770	2019-02-24	53	13	2	1	5
31	2019-03-03	129.051462	2019-03-03	47	13	6	0	6
32	2019-03-10	130.070409	2019-03-10	48	13	0	1	6
33	2019-03-17	131.938696	2019-03-17	50	17	1	0	9
34	2019-03-24	134.421098	2019-03-24	62	24	3	0	14
35	2019-03-31	129.810285	2019-03-31	41	13	1	0	13
36	2019-04-07	132.201572	2019-04-07	44	16	0	1	9
37	2019-04-14	132.025625	2019-04-14	62	16	1	0	10
38	2019-04-21	129.960000	2019-04-21	44	13	0	0	14
39	2019-04-28	124.811328	2019-04-28	47	14	1	1	8
40	2019-05-05	124.546484	2019-05-05	56	19	1	0	9
41	2019-05-12	120.023438	2019-05-12	65	18	2	0	9
42	2019-05-19	119.665723	2019-05-19	57	13	2	0	6
43	2019-05-26	118.929375	2019-05-26	68	19	2	0	7
44	2019-06-02	121.222813	2019-06-02	38	7	3	0	4
45	2019-06-09	126.421591	2019-06-09	49	15	1	0	6
46	2019-06-16	122.579063	2019-06-16	53	19	1	0	10
47	2019-06-23	118.106875	2019-06-23	76	21	1	0	11
48	2019-06-30	114.290566	2019-06-30	100	23	2	0	10
49	2019-07-07	115.996563	2019-07-07	87	27	2	0	11
50	2019-07-14	117.571875	2019-07-14	84	22	2	0	8
51	2019-07-21	127.450000	2019-07-21	75	27	1	1	10

		TUPRS	tüpraş: (Türkiye)	tüpraş hisse: (Türkiye)	tüpraş borsa: (Türkiye)	IST TUPRS: (Türkiye)	TUPRS: (Türkiye)	TUPRS_scaled
0	TUPRS	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	tüpraş: (Türkiye)	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	tüpraş hisse: (Türkiye)	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	tüpraş borsa: (Türkiye)	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	IST TUPRS: (Türkiye)	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...
51	tüpraş hisse: (Türkiye)	0.131337	0.473842	1.000000	0.280828	5.777144e-01	2.246624e-01	0.131337
	tüpraş borsa: (Türkiye)	-0.763396	0.779650	0.280828	1.000000	-4.472136e-01	-3.333333e-01	-0.763396
	IST TUPRS: (Türkiye)	0.820043	-0.130101	0.577714	-0.447214	1.000000e+00	5.958082e-16	0.820043
	TUPRS: (Türkiye)	-0.039166	-0.058183	0.224662	-0.333333	5.958082e-16	1.000000e+00	-0.039166
	TUPRS_scaled	1.000000	-0.669174	0.131337	-0.763396	8.200428e-01	-3.916619e-02	1.000000